GPT-OSS-120b/20b is probably the best you can run on your own hardware today. Be careful with the quantized versions though, as they're really horrible compared to the native MXFP4. I haven't looked in this particular case, but Ollama tends to hide their quantizations for some reason, so most people who could be running 20B with MXFP4, are still on Q8 and getting much worse results than they could.
The gpt-oss weights on Ollama are native mxfp4 (the same weights provided by OpenAI). No additional quantization is applied, so let me know if you're seeing any strange results with Ollama.
Most gpt-oss GGUF files online have parts of their weights quantized to q8_0, and we've seen folks get some strange results from these models. If you're importing these to Ollama to run, the output quality may decrease.
It's a different way of doing quantization (https://huggingface.co/docs/transformers/en/quantization/mxf...) but I think the most important thing is that OpenAI delivered their own quantization (the MXFP4 from OpenAI/GPT-OSS on HuggingFace, guaranteed correct) whereas all the Q8 and other quantizations you see floating around are community efforts, with somewhat uneven results depending on who done it.
Concretely from my testing, both 20B and 120B has a lot higher refusal rate with Q8 compared to MXFP4, and lower quality responses overall. But don't take my word for it, the 20B weights are tiny and relatively effortless to try both versions and compare yourself.
on the model description page they claim they support it:
Quantization - MXFP4 format
OpenAI utilizes quantization to reduce the memory footprint of the gpt-oss models. The models are post-trained with quantization of the mixture-of-experts (MoE) weights to MXFP4 format, where the weights are quantized to 4.25 bits per parameter. The MoE weights are responsible for 90+% of the total parameter count, and quantizing these to MXFP4 enables the smaller model to run on systems with as little as 16GB memory, and the larger model to fit on a single 80GB GPU.
Ollama is supporting the MXFP4 format natively without additional quantizations or conversions. New kernels are developed for Ollama’s new engine to support the MXFP4 format.
Ollama collaborated with OpenAI to benchmark against their reference implementations to ensure Ollama’s implementations have the same quality.
The default ones on Ollama are MXFP4 for the feed forward network and use BF16 for the attention weights. The default weights for llama.cpp quantize those tensors as q8_0 which is why llama.cpp can eek out a little bit more performance at the cost of worse output. If you are using this for coding, you definitely want better output.
You can use the command `ollama show -v gpt-oss:120b` to see the datatype of each tensor.
Can you be more specific? I've got LM Studio downloaded but it's not clear where are the official OpenAI releases? Are they all only available via transformers? The only one that shows up in search appears to be the distilled gpt-oss 20B...
The key thing I'm confident in is that 2-3 years from now there's going to be a model(s) and workflow that has comparable accuracy, perhaps noticeable (but tolerable) higher latency that can be run locally. There's just no reason to believe this isn't achievable.
Hard to understand how this won't make all of the solutions for existing use cases commodity. I'm sure 2-3 years from now there'll be stuff that seems like magic to us now -- but it will be more-meta, more "here's a hypothesis of a strategically valuable outcome and heres a solution (with market research and user testing done".
I think current performance and leading models will turn out to have been terrible indicators for future market leader (and my money will remain on the incumbents with the largest cash reserves (namely Google) that have invested in fundamental research and scaling).
